Although there have been considerable achievements in machine learning-based models, existing methods tend to overlook anomalies' special effects on real-world time series data. For instance,
LSTMs [1]} address the vanishing gradient problem via gate mechanism and have the ability to capture complex temporal dependencies [2]}. Yet, Khandelwal et al. [3]} show that even LSTMs have a limited ability to capture long-term dependencies, and their awareness of context degrades as
the length of the input sequence increases. Consequently, making them inefficient to capture and learn from rare occurrences or extreme events.
